In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality. One very common set of input documents includes photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations often result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, and not necessarily a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
Three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image. This is a common approach taken to reproduction. However, the output documents of such a system are sometimes not satisfactory to the ultimate customer.
In a second case of image enhancement, the image can always be processed. It turns out that an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved.
Many improvements can be made to an image, including exposure adjustment (described in previously filed U.S. patent application Ser. No. 08/132,973, filed Oct. 7, 1993), color balance correction (described in previously filed U.S. patent application Ser. No. 08/131,172, filed Oct. 4, 1993, and U.S. patent application Ser. No. 08/139,660, filed Oct. 22, 1993) or contrast enhancement (described in previously filed U.S. patent application Ser. No. 08/133,231, filed Oct. 7, 1993) Generally, these processing methods operate by modifying a set of reproduction curves (TRCs). The output image is achieved by using TRC curves, operating either on the luminance channel of an image expressed LC.sub.1 C.sub.2 coordinates, or preferably on each channel in a color density space description of the image in Red-Green-Blue (rgb) coordinates. One possible cascade or serial order for such processing is: 1) exposure correction, 2) contrast correction, and 3) color correction. One possible way of implementing such an arrangement is for each processor to determine the required correction, operate on the TRC curve, generate a new corrected image and use the histogram of the corrected image for the next processor.
A problem in cascading several operations that work on the image data is that subsequent processing modules have to receive the information of the operations of the prior modules in order to perform the correct modifications based on the image at that particular stage of proceeding. Accurate processing requires the prediction of the image resulting from prior processing, particularly where the TRC correction is calculated for the luminance channel, but applied to each rgb separation (a preferred mode). In cases where the processing is determined as a function of the image histogram, it is sufficient to predict the histogram of the image at every step of the processing. While in a black and white image, the modification of the histogram is deterministic and the altered histogram can be easily calculated; in color systems the use of the altered TRC curve of the three component color image leads to a modification of the histogram that cannot be calculated. The use of the calculated luminance histogram ignoring the color nature of the image would result in a poor approximation of the actual histogram. The effect of measuring in luminance terms and applying the TRC curve in RGB can be understood using an example of two pixels having the same luminance, but representing different colors (Xerox red, green, blue as described in the "Xerox Color Encoding Standard," XNSS 289005, 1989) in an 8 bit system: EQU Point1(r,g,b)=(10,30,50).fwdarw.luminance=26.2(given by0.253.times.10+0.684.times.30+0.063.times.50) EQU Point2(r,g,b)=(30,10,187).fwdarw.luminance=26.2(given by0.253.times.30+0.684.times.10+0.063.times.187)
Given a TRC that has a piecewise linear mapping of: EQU 0.ltoreq.input&lt;20 in.rarw.3.0.times.in EQU 20.ltoreq.input&lt;40 in.rarw.1.5.times.in EQU 40.ltoreq.input&lt;255 in.rarw.1.0.times.in
results in new pixel values: EQU Point1(r,g,b)=(30,45,50).fwdarw.luminance=41.5(given by0.253.times.30+0.684.times.45+0.063.times.50) EQU Point2(r,g,b)=(45,30, 187).fwdarw.luminance=43.7(given by0.253.times.45+0.684.times.30+0.063.times.187)
This example illustrates that pixels originally having identical luminance signals can have different luminance signals after processing the rgb separations. In order to get the actual luminance histogram at this point, a luminance signal would have to be re-derived or re-measured, by processing all data. As this process requires significant processing time for generation of the required histograms and TRC corrections, it is less than desirable. Implementations in software of the re-derived or re-measured histogram method would be particularly undesirable.
The reference cited, and specifically the several image processing applications owned by the assignee of the present application, are incorporated by reference herein.